Drowning and aquatic injuries dictionary
Bibliographic record
Abstract
BACKGROUND: Drowning is a significant public health issue with more than 320,000 deaths globally every year. These numbers are greatly underestimated, however, due to factors such as inadequate data collection, inconsistent categorization and failure to report in certain regions and cultures.The objective of this study was to develop a standardised drowning dictionary using a consensus-based approach. Through creation of this resource, improved clarity amongst stakeholders will be achieved and, as a result, so will our understanding of the drowning issue. METHODOLOGY: A list of terms and their definitions were created and sent to 16 drowning experts with a broad range of backgrounds across four continents and six languages. A review was conducted using a modified Delphi process over five rounds. A sixth round was done by an external panel evaluating the terms' content validity. RESULTS: The drowning dictionary included more than 350 terms. Of these, less than 10% had been previously published in peer review literature. On average, the external expert validity endorsing the dictionary shows a Scale Content Validity Index (S-CVI/Ave) of 0.91, exceeding the scientific recommended value. Ninety one percent of the items present an I-CVI (Level Content Validity Index) value considered acceptable (>0.78). The endorsement was not a universal agreement (S-CVI/UA: 0.44). CONCLUSION: The drowning dictionary provides a common language, and the authors envisage that its use will facilitate collaboration and comparison across prevention sectors, education, research, policy and treatment. The dictionary will be open to readers for discussion and further review at www.idra.world.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".